This application discloses an invention which is related, generally and in various embodiments, to a system and method for visualizing a structure of interest.
Medical imaging has long played a critical role in diagnosing and assessing various pathologies. Especially during the last decade, along with a rapid evolution of computer technologies, many new imaging modalities providing three-dimensional (3D) image datasets have been introduced and proven to be clinically useful. This broad spectrum of 3D image modalities is used to image a wide variety of internal features of patients.
One of the advantages of 3D image datasets, when compared to previous two dimensional (2D) datasets, is the flexible image visualization capability with which 3D datasets are characterized. Specifically, when utilizing a full 3D dataset, an operator can mathematically “slice” the target tissue along any arbitrary plane or other surface. This flexibility allows clinicians to observe pathologies from the exact point (or points) of view that are most useful for a given clinical situation.
One commonly employed imaging modality, optical coherence tomography (OCT), is a non-invasive, interferometric imaging modality that provides millimeter penetration at micrometer-scale axial and lateral resolution. OCT is particularly adaptable to opthalmologic applications. First generation OCT techniques provided 2D cross-sectional views of human retinas at a microscopic resolution (˜10 μm) in vivo. Due to its non-contact, non-invasive scanning, OCT quickly became an indispensable clinical test for retinal abnormalities including the leading causes of blindness: age-related macular degeneration, diabetic retinopathy, and glaucoma.
Spectral domain optical coherence tomography (SD-OCT) has gained increased utilization in opthalmologic applications. Compared to first generation OCT techniques, SD-OCT scans 60 times faster with a resolution (˜5 μm) that is twice as fine. With its fast scanning, SD-OCT allows 3D image dataset acquisition in a reasonably short period of time (e.g., approximately 1.5 seconds).
In conventional OCT techniques, when the eye is scanned, raw data (i.e., 3D OCT data) corresponding to the structure of the eye is captured. The raw data is subsequently processed into 3D image data (e.g., universal time-domain intensity data), from which various images associated with the eye can be generated. Such images include an OCT fundus image (FIG. 1), a conventional C-mode image (FIG. 2), a horizontal (x-z) cross-sectional image (FIG. 3), and a vertical (y-z) cross-sectional image (FIG. 4).
The natural spherical curvature of the eye (as well as certain other target structures) presents unique problems for opthalmologic applications of OCT, including applications of SD-OCT. Conventional C-mode images along the planes perpendicular to the scanning axis can provide an interesting perspective. However, due to the natural spherical curvature of the eye, and to various layered structures within the eye (e.g., retinal layers, choroid, etc.), a conventional C-mode image associated with an eye often includes multiple different structures, thereby making the conventional C-mode images very difficult to accurately interpret, even by a highly trained ophthalmologist.
Additionally, the natural spherical curvature of the eye is not the only source of artifactual distortion when attempting these types of image dataset acquisitions. Certain target structures, such as the eye, often experience rapid and involuntary movement. Such movement often distorts the raw data, which in turn distorts the 3D image data. When a conventional C-mode image is generated from the distorted 3D image data, the conventional C-mode image is very difficult to accurately interpret, even by a highly trained ophthalmologist.
Although a given conventional C-mode image may appear to be satisfactory for accurate interpretation, an associated cross-sectional image (e.g., a horizontal cross-sectional image, a vertical cross-sectional image, etc.) may show that the 3D image data utilized to generate the conventional C-mode image is distorted. For example, FIG. 5 shows four images generated from the same 3D image data set. The image in the upper left is a conventional C-mode image, the image in the upper right is a horizontal cross-sectional image, the image in the lower right is a vertical cross-sectional image, and the image in the lower left is the same conventional C-mode image as the one in the upper left. Even though the conventional C-mode images and the horizontal cross-sectional image appear to be satisfactory, the fair amount of eye movement shown in the vertical cross-sectional image indicates that the 3D image data set used to generate the images was distorted.
One approach utilized to try to reduce the problems caused by the natural curvature of the eye and/or movement of the eye during the scan is segmentation. Segmentation algorithms operate to segment the 3D image data into segments, where each segment corresponds to a given structure of interest (e.g., the retinal pigment epithelium, the internal limiting membrane, etc.). After the structure of interest is identified, the 3D image data is sampled along the corresponding segment (which has an arbitrary thickness). The sampled 3D image data is utilized to generate a “segmented” C-mode image which only contains structures from the same segment. In general, the segmented C-mode images provide more accurate visualization than conventional C-mode images. However, it is well known that segmentation algorithms frequently fail to detect proper structures, especially in cases where retinal pathologies are present, leading to the poor quality segmented C-mode images.